1. Field
This application relates to acoustic recognition.
2. Description of Related Art
The technology of recognizing acoustic signatures of various sources has gained increasing interest as applications based on sound recognition continue to proliferate. Detection of objects such as running vehicles based on acoustic signatures is one such application. From an acoustic waveform of a running vehicle, for example, characteristic patterns in a “training mode” may be extracted, associated with the vehicle, and stored for subsequent comparison with candidate vehicle sounds in a real world environment. Where a “match” is found to exist, a positive identification may be deemed to have occurred.
Among the significant challenges faced by practitioners include developing an acoustic recognition technique that can function effectively in this real world environment. Under typical circumstances in such an environment, a variety of environmental sounds and noises, whether expected or unexpected, inevitably will accompany the acoustic waveform associated with the vehicle of interest. Such sounds may include, for example, wind, rain, the scuffling of feet, human voices, the chirping of a bird, etc. Still other sounds may be machine-generated, such as the sound of an industrial plant located in the vicinity or of a jet taking off. To deal with these problems, a noise-robust acoustic recognition system for vehicles is needed that can quickly and effectively differentiate these types of sounds from potential vehicle sounds to identify whether a vehicle or other object of interest is present in the monitored area.
The running vehicle itself may too contribute to the environmental variations, such as, for example, when the vehicle is being driven in the rain or with worn out tires. The acoustic sound of interest from a running vehicle is a complicated one that is affected by multiple factors, such as vehicle types, gearing, number of cylinders, choice of the muffler, state of repair of the vehicle, moving speed, distance from the microphone, and the road on which the vehicle travels. To date, no technique has been developed to effectively account for these variables to provide the needed discrimination between background sounds on one hand, and sounds of interest on the other hand.
Further, existing approaches are incapable of effectively differentiating between different arbitrary classes of vehicles, which would provide an important feature in a growing number of applications.